


# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)


@flax.struct.dataclass
class     __UpperCAmelCase							:


     """simple docstring"""
     _lowerCamelCase   =      42

     # setable values
     _lowerCamelCase   =      42
     _lowerCamelCase   =      42
     _lowerCamelCase   =      None


     @classmethod
     def 	snake_case_  (	cls  ,       __A  ,       __A  ,       __A    ):
        return cls(common=__A  ,       init_noise_sigma=__A  ,       timesteps=__A    )







@dataclass
class     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      42







class     __UpperCAmelCase							(       __A ,					__A							):


     """simple docstring"""
     _lowerCamelCase   =      [e.name for e in FlaxKarrasDiffusionSchedulers]

     _lowerCamelCase   =      42


     @property
     def 	snake_case_  (	self    ):
        return True


     @register_to_config
     def __init__(	self  ,       __A = 1000  ,       __A = 0.0001  ,       __A = 0.02  ,       __A = "linear"  ,       __A = None  ,       __A = "fixed_small"  ,       __A = True  ,       __A = "epsilon"  ,       __A = jnp.floataa  ,       ):
        __a					=  dtype


     def 	snake_case_  (	self  ,       __A = None    ):
        if common is None:
           __a					=  CommonSchedulerState.create(self    )

        # standard deviation of the initial noise distribution
        __a					=  jnp.array(1.0  ,       dtype=self.dtype    )

        __a					=  jnp.arange(0  ,       self.config.num_train_timesteps    ).round()[::-1]

        return DDPMSchedulerState.create(
            common=__A  ,       init_noise_sigma=__A  ,       timesteps=__A  ,       )


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A = None    ):
        return sample


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A = ()    ):
        __a					=  self.config.num_train_timesteps // num_inference_steps
        # creates integer timesteps by multiplying by ratio
        # rounding to avoid issues when num_inference_step is power of 3
        __a					=  (jnp.arange(0  ,       __A    ) * step_ratio).round()[::-1]

        return state.replace(
            num_inference_steps=__A  ,       timesteps=__A  ,       )


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A=None  ,       __A=None    ):
        __a					=  state.common.alphas_cumprod[t]
        __a					=  jnp.where(t > 0  ,       state.common.alphas_cumprod[t - 1]  ,       jnp.array(1.0  ,       dtype=self.dtype    )    )

        # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
        # and sample from it to get previous sample
        # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
        __a					=  (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

        if variance_type is None:
           __a					=  self.config.variance_type

        # hacks - were probably added for training stability
        if variance_type == "fixed_small":
           __a					=  jnp.clip(__A  ,       a_min=1E-20    )
        # for rl-diffuser https://arxiv.org/abs/2205.09991
        elif variance_type == "fixed_small_log":
           __a					=  jnp.log(jnp.clip(__A  ,       a_min=1E-20    )    )
        elif variance_type == "fixed_large":
           __a					=  state.common.betas[t]
        elif variance_type == "fixed_large_log":
           # Glide max_log
           __a					=  jnp.log(state.common.betas[t]    )
        elif variance_type == "learned":
           return predicted_variance
        elif variance_type == "learned_range":
           __a					=  variance
           __a					=  state.common.betas[t]
           __a					=  (predicted_variance + 1) / 2
           __a					=  frac * max_log + (1 - frac) * min_log

        return variance


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A = None  ,       __A = True  ,       ):
        __a					=  timestep

        if key is None:
           __a					=  jax.random.PRNGKey(0    )

        if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
           __a	,    __a					=  jnp.split(__A  ,       sample.shape[1]  ,       axis=1    )
        else:
           __a					=  None

        # 1. compute alphas, betas
        __a					=  state.common.alphas_cumprod[t]
        __a					=  jnp.where(t > 0  ,       state.common.alphas_cumprod[t - 1]  ,       jnp.array(1.0  ,       dtype=self.dtype    )    )
        __a					=  1 - alpha_prod_t
        __a					=  1 - alpha_prod_t_prev

        # 2. compute predicted original sample from predicted noise also called
        # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
        if self.config.prediction_type == "epsilon":
           __a					=  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
        elif self.config.prediction_type == "sample":
           __a					=  model_output
        elif self.config.prediction_type == "v_prediction":
           __a					=  (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
        else:
           raise ValueError(
               f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '''
               """ for the FlaxDDPMScheduler."""    )

        # 3. Clip "predicted x_0"
        if self.config.clip_sample:
           __a					=  jnp.clip(__A  ,       -1  ,       1    )

        # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
        # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
        __a					=  (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
        __a					=  state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

        # 5. Compute predicted previous sample µ_t
        # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
        __a					=  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

        # 6. Add noise
        def random_variance():
           __a					=  jax.random.split(__A  ,       num=1    )
           __a					=  jax.random.normal(__A  ,       shape=model_output.shape  ,       dtype=self.dtype    )
           return (self._get_variance(__A  ,       __A  ,       predicted_variance=__A    ) ** 0.5) * noise

        __a					=  jnp.where(t > 0  ,       random_variance()  ,       jnp.zeros(model_output.shape  ,       dtype=self.dtype    )    )

        __a					=  pred_prev_sample + variance

        if not return_dict:
           return (pred_prev_sample, state)

        return FlaxDDPMSchedulerOutput(prev_sample=__A  ,       state=__A    )


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A  ,       __A  ,       ):
        return add_noise_common(state.common  ,       __A  ,       __A  ,       __A    )


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A  ,       __A  ,       ):
        return get_velocity_common(state.common  ,       __A  ,       __A  ,       __A    )


     def __len__(	self    ):
        return self.config.num_train_timesteps







'''simple docstring'''


def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				print("""\nThe shortest path matrix using Floyd Warshall algorithm\n"""	)
				for i in range(SCREAMING_SNAKE_CASE__	):
								for j in range(SCREAMING_SNAKE_CASE__	):
												if dist[i][j] != float("""inf"""	):
																print(int(dist[i][j]	)				, end="""\t"""	)
												else:
																print("""INF"""				, end="""\t"""	)
								print()







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Any							= [[float("""inf"""	) for _ in range(SCREAMING_SNAKE_CASE__	)] for _ in range(SCREAMING_SNAKE_CASE__	)]

				for i in range(SCREAMING_SNAKE_CASE__	):
								for j in range(SCREAMING_SNAKE_CASE__	):
												_SCREAMING_SNAKE_CASE					:			List[str]							= graph[i][j]

												# check vertex k against all other vertices (i, j)
				for k in range(SCREAMING_SNAKE_CASE__	):
								# looping through rows of graph array
								for i in range(SCREAMING_SNAKE_CASE__	):
												# looping through columns of graph array
												for j in range(SCREAMING_SNAKE_CASE__	):
																if (
																    dist[i][k] != float("""inf"""	)
																    and dist[k][j] != float("""inf"""	)
																    and dist[i][k] + dist[k][j] < dist[i][j]
																):
																				_SCREAMING_SNAKE_CASE					:			int							= dist[i][k] + dist[k][j]

				_print_dist(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)
				return dist, v


if __name__ == "__main__":
				UpperCAmelCase_						:  str				  =						int(input('Enter number of vertices: '))
				UpperCAmelCase_						:  Union[str, Any]				  =						int(input('Enter number of edges: '))

				UpperCAmelCase_						:  Dict				  =						[[float('inf') for i in range(v)] for j in range(v)]

				for i in range(v):
								UpperCAmelCase_						:  Union[str, Any]				  =						0.0

								# src and dst are indices that must be within the array size graph[e][v]
								# failure to follow this will result in an error
				for i in range(e):
								print('\nEdge ', i + 1)
								UpperCAmelCase_						:  int				  =						int(input('Enter source:'))
								UpperCAmelCase_						:  List[str]				  =						int(input('Enter destination:'))
								UpperCAmelCase_						:  List[str]				  =						float(input('Enter weight:'))
								UpperCAmelCase_						:  int				  =						weight

				floyd_warshall(graph, v)

				# Example Input
				# Enter number of vertices: 3
				# Enter number of edges: 2

				# # generated graph from vertex and edge inputs
				# [[inf, inf, inf], [inf, inf, inf], [inf, inf, inf]]
				# [[0.0, inf, inf], [inf, 0.0, inf], [inf, inf, 0.0]]

				# specify source, destination and weight for edge #1
				# Edge  1
				# Enter source:1
				# Enter destination:2
				# Enter weight:2

				# specify source, destination and weight for edge #2
				# Edge  2
				# Enter source:2
				# Enter destination:1
				# Enter weight:1

				# # Expected Output from the vertice, edge and src, dst, weight inputs!!
				# 0		INF	INF
				# INF	0	2
				# INF	1	0




